Beginner Lessons

The Importance of Real-Time ML and How it Became a Critical System

The Importance of Connecting Data Science Projects to Business Goals

Evolution of Infrastructure in Machine Learning // Simon Thompson // MLOps Shorts

Machine Learning Care // Matthew Dombrowski // MLOps Podcast #142 clip

Unlocking the Potential of Data // Ethan Rosenthal // MLOps Coffee Sessions #131 short clip

Large Language Models in NLP Workflow

The Challenges of Integrating Multiple Tasks into One Model in MLOps // Ethan Rosenthal

Advice for Technically Capable People Who Don't Know Where to Start

High Precision Rules and Machine Learning in Cybersecurity // Jeremy Thomas Jordan // MLOps Shorts

MLOps vs ML-As-Service // Jill Chase and Manmeet Gujral // MLOps Podcast #143 clip

The Use of Rules Engines in Cybersecurity Threat Detection // Jeremy Thomas Jordan // MLOps Shorts

Team Aurora: Accelerating ML with Kubeflow // Ankit, Vinay, and Maurizio // MLOps Meetup #119

Exploring the Advancements and Potential of Machine Learning

ML Ops Market: Slow and Steady Growth Amid Plateau Hype

Verticalization and Infrastructure Solutions in Machine Learning

Real-time Machine Learning: Ease of Use and Portability

Real-time Machine Learning: Is it Always Worth the Effort?

Exploring Customer Needs for Electric Vehicles through Twitter Data Mining #shorts

The Hype and Realities of Machine Learning #shorts

Accelerating Data Centric Development with GPT-3: Smaller and Cheaper Deployment Models

The Adaptation Gap: Bridging the Gap between Generalist and Specialized Models

Challenges and Concerns of Using GPT-3 in Enterprise Settings // Alex Ratner // MLOps Podcast Shorts

The Motivation for MLOps // Steven Fines // MLOps Meetup #118

Foundational Models are the Future but... with Alex Ratner CEO of Snorkel AI // MLOps Podcast #139

Vertex AI Workshop // Sascha Heyer // MLOps Meetup #117

Facebook Marketplace Machine Learning System Design Review

Machine Learning Operations — What is it and Why Do We Need It? // Niklas Kühl // MLOps Podcast #137

Building an Open Source MLOps Stack with ZenML Part 2 // Hamza Tahir // MLOps Meetup # 116

Systems Engineer Navigating the World of ML // Andrew Dye // MLOps Podcast #136

"Real-Time" ML: Features and Inference // Sasha Ovsankin and Rupesh Gupta // MLOps Podcast #135

Building Threat Detection Systems: An MLE's Perspective // Jeremy Jordan // MLOps Podcast #134

Driving ML Data Quality with Data Contracts // Andrew Jones // MLOps Meetup #115

MLOps in Practice: Common Challenges and Lessons Learned / Marouen Hizaoui & Mo Basirati/Meetup #114

Real-time Machine Learning with Chip Huyen // MLOps Coffee Sessions #133

What is Data / ML Like on League? // Ian Schweer // MLOps Coffee Sessions #132

Let's Continue Bundling into the Database // Ethan Rosenthal // MLOps Coffee Sessions #131

Unleashing Sensitive Datasets with Distributed Data Science // Blaise Thomson // MLOps Meetup #112

MLOps for Ad Platforms // Andrew Yates // MLOps Coffee Sessions #130

Applying DevOps Practices in Data and ML Engineering // Antoni Ivanov // MLOps Meetup #111

Voice and Language Tech // Catherin Breslin // Coffee Sessions #129

Managing Machine Learning Projects // Simon Thompson // MLOps Coffee Sessions #128

Obtain New Insights on Model Behavior with Fiddler // Danny Brock // MLOps Meetup #110

Reliable Machine Learning // Niall Murphy & Todd Underwood // Coffee Sessions #127

ML Unicorn Start-up Investor Tells-IT-All // George Mathew // MLOps Coffee Sessions #126

Databricks Model Serving V2 // Rafael Pierre // Coffee Sessions #125

Monitoring Unstructured Data // Aparna Dhinakaran & Jason Lopatecki // Lightning Sessions #2

Trustworthy Machine Learning // Kush Varshney // Coffee Sessions #124

RECOMMENDER SYSTEM: Why They Update Models 100 Times a Day // Gleb Abroskin // MLOps Coffee Ses #123